Multi-source seismic data transmission method

Abstract

The invention relates to the technical field of data transmission, in particular to a multi-source seismic data transmission method, which comprises the following steps of: setting labels for departments by determining data demand characterization values of the departments, determining seismic disaster coefficients of the departments to divide seismic disaster areas, and identifying seismic data in combination with real-time disaster semantics for high-risk seismic disaster areas. Analyzing the dependency relationship between the demanded data of the departments and the seismic data; determining a data dependency characterization value; analyzing the seismic influence range of the place to determine a regional disaster situation characterization value; calculating a seismic data utility value; the mapping conditions are analyzed to determine that the department receives the data packet. According to the invention, the seismic data is pre-packaged and pushed before being pulled by analyzing the demand urgency of each department for the seismic data, so that the data acquisition speed and the data transmission accuracy of related departments are improved.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, and in particular to a method for transmitting multi-source seismic data. Background Technology

[0002] Multi-source seismic data is a core foundation for geological exploration and earthquake monitoring. With advancements in detection technology, the simultaneous or alternating operation of multiple seismic sources has led to an exponential increase in the demands for data volume, real-time performance, and reliability. The data volume generated by a single exploration mission often reaches TB or even PB levels. On the one hand, traditional transmission methods using a single network struggle to adapt to the varying bandwidth requirements of multi-source data, easily leading to link congestion. On the other hand, complex terrain results in unstable wireless signals, and traditional transmission protocols lack anti-interference mechanisms, resulting in high packet loss rates and affecting the accuracy of subsequent data processing. Therefore, transmission technology has evolved into digital bus transmission under distributed acquisition, ultimately developing into a modern transmission system based on packet switching and IP networking protocols, using high-bandwidth, low-latency optical fiber as the physical layer. This has solved key problems such as real-time transmission of massive amounts of data, precise synchronization, and control.

[0003] Chinese Patent Publication No. CN119064989A discloses a method for integrating and sharing multi-source seismic data. The method includes: collecting seismic data from the same data source into a single data set based on all data sources monitored for seismic data; filtering and classifying seismic data from different data sources according to data encoding formats to obtain data sets of different target seismic data types; extracting data features from the seismic data within each data set, calculating seismic response coefficients based on the seismic response degree characterized by these features, and integrating these seismic response coefficients to obtain seismic response data packets; and sequentially sending the sub-data packets in the shared sequence to the sharing user terminal using the data transmission time of different transmission paths and the number of sub-data packets that can be sent. This invention ensures the widespread dissemination and effective utilization of seismic data and has significant advantages and application prospects in promoting earthquake scientific research.

[0004] Chinese Patent Publication No. CN109495279A discloses an active data transmission system for seismic exploration systems. The seismic exploration system has multiple acquisition nodes connected to a transmission line. The active data transmission system supplies power to each acquisition node and transmits data between nodes. The active transmission system includes a power supply end and a power receiving end. The power supply end is configured on the preceding acquisition node, and the power receiving end is configured on the following acquisition node. Both the power supply end and the power receiving end have power modules. This invention's active data transmission system for seismic exploration systems uses power and data coupling for multiplexing transmission, reducing the weight and number of cable cores in the data transmission line. After implementation, this system can save on cables in seismic exploration systems, conserving manpower and resources, and greatly facilitating field operations.

[0005] However, the following problems still exist in the existing technology. The high concurrency of earthquake data access by multiple departments after an earthquake can easily lead to reduced data retrieval and transmission efficiency, causing delays in updating critical disaster data for departments that require real-time and rapid data, and failing to meet the real-time data requirements of emergency response. Summary of the Invention

[0006] To address this issue, the present invention provides a method for transmitting multi-source earthquake data, which overcomes the problem that high concurrent access to earthquake data by multiple departments after an earthquake can easily lead to reduced data retrieval and transmission efficiency, cause delays in updating critical disaster data for departments that require real-time and rapid data, and fail to meet the real-time data requirements of emergency response.

[0007] To achieve the above objectives, the present invention provides a method for transmitting multi-source seismic data, comprising: Historical response parameters of several departments are obtained, including response time, response keywords, and disaster response type, in order to determine the data demand characterization value of each department and to set push labels for each department. Based on the settings of the push tags, earthquake parameters, including magnitude, focal depth, and location, are collected in real time to calculate the earthquake disaster coefficient of each location and delineate earthquake-affected areas. Based on the division of earthquake-affected areas, for high-risk earthquake-affected areas, combined with real-time disaster semantic recognition earthquake data, the dependency relationship between departmental demand data set as push tags and earthquake data is analyzed to determine the data dependency characterization value, and the earthquake impact range of the location is analyzed to determine the regional disaster situation characterization value. The earthquake data utility value is calculated by combining the data dependency characterization value and the regional disaster situation characterization value. The earthquake data that meets the preset conditions is packaged and the successfully packaged data package is pushed to the edge node of the corresponding department. Based on the data packet push status of the edge node, the pull matching degree between the department and the data packet is calculated, and it is analyzed whether the data packet meets the mapping conditions to determine whether the department receives the data packet. The preset condition is that the seismic data utility value is greater than the seismic data utility value threshold.

[0008] Furthermore, the process of determining the data requirement representation values ​​for each of the aforementioned departments includes, The ratio of the difference between the response time and the baseline response time is determined as the time influence factor; The matching degree between the response keywords and core earthquake terms is determined as the keyword influence factor; The correlation between the disaster response type and the earthquake type was determined as the type influence factor; The time influence factor, the keyword influence factor, and the type influence factor are normalized. The weighted sum of the normalized time influence factor, keyword influence factor, and type influence factor is determined as the data demand characterization value.

[0009] Furthermore, the labeling of each of the aforementioned departments is performed, wherein... If the data demand representation value is greater than or equal to the data demand representation value threshold, then the department corresponding to the data demand representation value is determined to set a push label; If the data demand representation value is less than the data demand representation value threshold, then the department corresponding to the data demand representation value is determined to set a normal retrieval label.

[0010] Furthermore, the process of calculating the earthquake damage coefficient for each location includes, The ratio of the magnitude to the magnitude of the earthquake's impact is determined as the magnitude impact factor; The ratio of the focal depth to the safe focal depth is determined as the depth influence factor; The ratio of the intensity of the earthquake at the location to the seismic fortification intensity is determined as the seismic resistance factor. The weighted sum of the magnitude influence factor, the depth influence factor, and the seismic resistance factor is determined to be the earthquake disaster coefficient.

[0011] Furthermore, the division of earthquake-affected areas, wherein, If the earthquake disaster coefficient is greater than the earthquake disaster coefficient threshold, then the earthquake-affected area is classified as a high-risk earthquake-affected area. If the earthquake disaster coefficient is less than or equal to the earthquake disaster coefficient threshold, then the earthquake-affected area is classified as a low-risk earthquake-affected area.

[0012] Furthermore, the process of determining the data dependency representation value includes, Determine the demand keywords for the departmental data and the seismic descriptive terms for the earthquake data. Calculate the semantic relevance between the demand keywords and the earthquake descriptive terms; The semantic relevance is determined as a data dependency representation value.

[0013] Furthermore, the process of analyzing the earthquake impact range at the location to determine the regional disaster situation characterization value includes, Obtain the historical impact range of several earthquakes of the same magnitude to determine earthquake impact factors; The average of the building vulnerability index and the earthquake impact factor at the location is determined as the regional disaster situation characterization value.

[0014] Furthermore, the process of calculating the utility value of seismic data includes, The ratio of the data dependency representation value to the benchmark data dependency representation value is determined as the first utility factor; The ratio of the disaster situation characterization value of the aforementioned region to the disaster situation characterization value of the benchmark region is determined as the second utility factor; The weighted sum of the first utility factor and the second utility factor is determined to be the utility value of the seismic data.

[0015] Furthermore, the process of calculating the matching degree between the department and the data packet includes, Determine the frequency and processing speed of the department's access to the data packet within a predetermined time period; The ratio of the access frequency to the processing speed is determined as the pull matching degree.

[0016] Further, the analysis determines whether the data packet satisfies the mapping conditions to ascertain whether the department receives the data packet, wherein, If the pull matching degree is greater than the pull matching degree threshold, then the data packet is determined to meet the mapping condition, and the department receives the data packet; If the pull matching degree is less than or equal to the pull matching degree threshold, then the data packet is determined not to meet the mapping conditions, and the department does not receive the data packet.

[0017] Compared with existing technologies, this invention determines the data demand characteristics of each department, assigns labels to each department, classifies earthquake-affected areas by determining the earthquake disaster coefficient of each location, and for high-risk earthquake-affected areas, it combines real-time disaster semantic recognition earthquake data to analyze the dependency relationship between the department's demand data and earthquake data, determines the data dependency characteristics, analyzes the earthquake impact range of the location to determine the regional disaster situation characteristics, calculates the earthquake data utility value, packages the demand data and pushes it to the edge nodes of the corresponding departments, calculates the pull matching degree between departments and data packets, and analyzes mapping conditions to determine which departments receive the data packets. This invention improves the data acquisition speed and data transmission accuracy of relevant departments by analyzing the urgency of each department's demand for earthquake data and pre-packaging and pushing earthquake data before data pull.

[0018] In particular, by pre-setting differentiated push tags for each department to distinguish the urgency of their data needs, this invention addresses the issue that in reality, when disasters such as earthquakes occur, multiple departments often concurrently request information and data. However, the urgency of data needs varies among departments. If all departments adopt a homogeneous data transmission strategy, not only will the network's "first-come, first-served" or "equal competition" scheduling principles cause data packets to enter the same queue at routing nodes, forcing high-urgency tasks to queue with low-urgency tasks, leading to a sharp increase in end-to-end latency and delaying decision-making and response, but resource allocation may also cause shared bandwidth and computing resources to be occupied by a large number of non-critical requests, resulting in resource lockout. While the transmission system may appear busy and have high throughput on a macro level, the efficiency of critical data transmission is severely diluted on a micro level, failing to meet the timeliness requirements of emergency command and further amplifying disaster losses. Based on this, this invention considers pre-calculating the data demand characteristics of each department and configuring different tags for each department, providing data and theoretical basis for subsequent matching with earthquake-affected areas to improve the speed and accuracy of data transmission.

[0019] In particular, while earthquake-affected areas are classified by calculating earthquake damage coefficients, the uneven distribution of earthquake disasters in reality leads to significant differences in the severity of damage across different regions. Using a uniform, undifferentiated data packaging and transmission method would not only reduce data accuracy, forcing lightly affected areas to receive irrelevant information while severely affected areas lack in-depth data support, but also cause an imbalance in network resource allocation. A massive influx of generalized data could easily clog communication links, delaying the transmission of critical information to severely affected areas and missing the golden window for emergency response. Therefore, this invention considers calculating earthquake damage coefficients before data transmission and using this calculation to classify earthquake-affected areas. High-risk areas are specifically identified and analyzed, providing a crucial theoretical basis for subsequent calculations of earthquake data utility values. By achieving precise matching of data delivery, the targeting and accuracy of emergency data transmission are improved, as well as the data acquisition speed and transmission accuracy for relevant departments.

[0020] In particular, by calculating the utility value of earthquake data and packaging the data, a data and theoretical basis is provided for subsequent calculation of matching degree. In practice, due to the large scale and uneven value density of multi-source heterogeneous post-earthquake data, if all data is transmitted directly without performing data utility value analysis, not only will high-timeliness and high-priority core disaster information be overwhelmed by a large amount of low-value data, affecting decision-making efficiency, but also the limited communication bandwidth and computing resources will be occupied by redundant data, hindering the real-time transmission of key information in severely affected areas. Furthermore, due to the large amount of complex data transmitted to related departments, the corresponding departments need to spend additional resources to filter effective information, thereby reducing the agility of emergency response. Based on this, this invention considers calculating the utility value of earthquake data, processing data with low relevance and no impact on monitoring locations before data transmission, and only performing subsequent analysis on a portion of the data, thereby improving the accuracy of data analysis and increasing the data acquisition speed and data transmission accuracy of relevant departments.

[0021] In particular, by calculating the pull matching degree, the strength of the correlation between data and departmental needs can be quantified, thereby providing a basis for data delivery decisions. However, in actual business scenarios, some data access behaviors of departments may only be to meet one-time or occasional needs. If they are judged as high-matching objects and mechanically pushed data simply because they have a history of pull records, it will not only cause the network channel and departmental receiving terminals to be occupied by a large amount of low-value data, resulting in significant resource waste, but also cause information overload to drown out truly critical data, thereby reducing the decision-making efficiency and response agility of the overall emergency command system. Based on this, the present invention considers introducing a dynamic pull matching degree calculation mechanism. By comprehensively considering access frequency and processing speed indicators, the actual pull status between data packets and departments can be determined, distinguishing between occasional access and continuous needs, improving the accuracy of data matching, so as to complete the targeted push of data, and improving the data acquisition speed and data transmission accuracy of relevant departments. Attached Figure Description

[0022] Figure 1 A schematic diagram illustrating the steps of a multi-source seismic data transmission method according to an embodiment of the invention; Figure 2 A logic block diagram illustrating the labeling of each of the aforementioned departments, as per an embodiment of the invention; Figure 3 This is a logic block diagram illustrating the division of earthquake-affected areas according to an embodiment of the invention. Figure 4 The following is a logical block diagram illustrating the analysis of whether the data packet satisfies the mapping conditions in an embodiment of the invention to determine the logic of the department receiving the data packet. Detailed Implementation

[0023] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0024] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating the steps of a multi-source seismic data transmission method according to an embodiment of the invention. The multi-source seismic data transmission method of the present invention includes: Step S1: Obtain historical response parameters from several departments, including response time, response keywords, and disaster response type, to determine the data demand characterization value of each department, and to set push tags for each department. Step S2: Based on the settings of the push tags, collect earthquake parameters in real time, including magnitude, focal depth, and location, to calculate the earthquake disaster coefficient of each location and delineate earthquake-affected areas. Step S3: Based on the division results of the earthquake-affected areas, for high-risk earthquake-affected areas, combined with real-time disaster semantic recognition earthquake data, analyze the dependency relationship between the departmental demand data set as push tags and the earthquake data, determine the data dependency characterization value, and analyze the earthquake impact range of the location to determine the regional disaster situation characterization value. Step S4: Calculate the earthquake data utility value by combining the data dependency characterization value and the regional disaster situation characterization value; package the earthquake data that meets the preset conditions; and push the successfully packaged data package to the edge node of the corresponding department. Step S5: Based on the data packet push status of the edge node, calculate the pull matching degree between the department and the data packet, analyze whether the data packet meets the mapping conditions, and determine whether the department receives the data packet; The preset condition is that the seismic data utility value is greater than the seismic data utility value threshold.

[0026] Specifically, there are no restrictions on the sources of historical response parameters, such as open-source earthquake databases, officially published disaster response record manuals, and de-identified institutional historical case databases. Of course, those skilled in the art can also use other methods, as long as the required data can be obtained, which will not be elaborated here.

[0027] It is understandable that the push notification tags are digital identifiers, not physical entity tags, and are only used to distinguish departments.

[0028] Specifically, there are no restrictions on the specific methods for semantic recognition of earthquake data. For example, it can be done by using natural language processing technology to analyze the descriptive text about earthquake sensations and damage phenomena and automatically extract key information. Alternatively, it can be done by using machine learning models to train the system to recognize the correspondence between descriptions such as "houses shaking" and "chandeliers swaying" and earthquake intensity. Any reasonable approach is acceptable and will not be elaborated further.

[0029] Specifically, the process of determining the data requirement representation values ​​for each of the aforementioned departments includes, The ratio of the difference between the response time and the baseline response time is determined as the time influence factor; The matching degree between the response keywords and core earthquake terms is determined as the keyword influence factor; The correlation between the disaster response type and the earthquake type was determined as the type influence factor; The time influence factor, the keyword influence factor, and the type influence factor are normalized. The weighted sum of the normalized time influence factor, keyword influence factor, and type influence factor is determined as the data demand characterization value.

[0030] Specifically, the baseline response time is calculated in advance by obtaining several historical response times of the corresponding department and determining the average of each historical response time as the baseline response time.

[0031] Specifically, there are no restrictions on how to determine the core vocabulary related to earthquakes. For example, in practice, it can be a set of high-frequency words and key phrases extracted from disaster reports, rescue instructions and official announcements of several earthquake events through text mining technology (such as TF-IDF). Of course, it can also be other official data, as long as it is reasonable. This will not be elaborated further.

[0032] Specifically, there are no restrictions on the method of obtaining earthquake types. For example, they can be obtained from earthquake early warning information released by official agencies. Of course, those skilled in the art can also use other methods to obtain them, as long as they are reasonable. This will not be elaborated further.

[0033] Specifically, the time impact factor, keyword impact factor, and type impact factor are uniformly mapped to the [0,1] interval to complete the normalization process.

[0034] Specifically, the sum of the weight coefficients of the time impact factor, keyword impact factor, and type impact factor is 1. When adjusting the weights, considering that the timeliness of departmental response data is the most critical factor, the weight coefficient of the time impact factor is set to 0.4, the weight coefficient of the keyword impact factor is 0.3, and the weight coefficient of the type impact factor is 0.3.

[0035] Please see Figure 2 , Figure 2 This is a logic block diagram illustrating the labeling of each of the departments according to an embodiment of the invention. Specifically, labels are set for each of the departments, wherein... If the data demand representation value is greater than or equal to the data demand representation value threshold, then the department corresponding to the data demand representation value is determined to set a push label; If the data demand representation value is less than the data demand representation value threshold, then the department corresponding to the data demand representation value is determined to set a normal retrieval label.

[0036] Specifically, the data demand representation value threshold represents a boundary of the urgency of a department's data demand. It is calculated in advance by obtaining historical data demand representation values ​​corresponding to several departments with several urgent data demands in advance. The average value of each historical data demand representation value and the product of the demand precision are determined as the data demand representation value threshold. The demand precision is selected within the range [0.8, 1.0]. In practice, in order to improve the accuracy of push, the demand precision is determined to be 0.9.

[0037] Specifically, by pre-setting differentiated push tags for each department to distinguish the urgency of their data needs, this invention addresses the issue that in reality, when disasters such as earthquakes occur, multiple departments often concurrently request information and data. However, the urgency of data needs varies among departments. If all departments adopt a homogeneous data transmission strategy, not only will the network's "first-come, first-served" or "equal competition" scheduling principles cause data packets to enter the same queue at routing nodes, forcing high-urgency tasks to queue with low-urgency tasks, leading to a sharp increase in end-to-end latency and delaying decision-making and response, but resource allocation may also cause shared bandwidth and computing resources to be occupied by a large number of non-critical requests, resulting in resource lockout. While the transmission system may appear busy and have high throughput on a macro level, the efficiency of critical data transmission is severely diluted on a micro level, failing to meet the timeliness requirements of emergency command and further amplifying disaster losses. Based on this, this invention considers pre-calculating the data demand characteristics of each department and configuring different tags for each department, providing data and theoretical basis for subsequent matching with earthquake-affected areas, thereby improving the speed and accuracy of data transmission.

[0038] Specifically, the process of calculating the earthquake damage coefficient for various locations includes, The ratio of the magnitude to the magnitude of the earthquake's impact is determined as the magnitude impact factor; The ratio of the focal depth to the safe focal depth is determined as the depth influence factor; The ratio of the intensity of the earthquake at the location to the seismic fortification intensity is determined as the seismic resistance factor. The weighted sum of the magnitude influence factor, the depth influence factor, and the seismic resistance factor is determined to be the earthquake disaster coefficient.

[0039] Specifically, the earthquake impact magnitude is calculated in advance by obtaining the magnitudes of several historical earthquakes that occurred but did not cause any impact, and determining the average of the magnitudes of these historical earthquakes as the earthquake impact magnitude.

[0040] Specifically, the safe focal depth is calculated in advance by obtaining the historical focal depths of several earthquakes that occurred without causing damage, and determining the average of these historical focal depths as the safe focal depth.

[0041] Specifically, the seismic fortification intensity is calculated in advance by obtaining the historical intensity of several earthquakes that occurred and caused damage to buildings, and determining the average value of each historical intensity as the seismic fortification intensity.

[0042] Specifically, the sum of the weighting coefficients of the magnitude influence factor, depth influence factor, and seismic resistance factor is 1. When adjusting the weights, considering that the earthquake's own intensity is a direct factor affecting the disaster state, the weighting coefficient of the magnitude influence factor is determined to be 0.4, the weighting coefficient of the depth influence factor is 0.3, and the weighting coefficient of the seismic resistance factor is 0.3.

[0043] Specifically, while earthquake-affected areas are delineated by calculating earthquake damage coefficients, the uneven distribution of earthquake disasters in reality leads to significant differences in the severity of damage across different regions. Using a uniform, undifferentiated data packaging and transmission method would not only reduce data accuracy, forcing lightly affected areas to receive irrelevant information while severely affected areas lack in-depth data support, but also cause an imbalance in network resource allocation. A massive influx of generalized data could easily clog communication links, delaying the transmission of critical information to severely affected areas and missing the golden window for emergency response. Therefore, this invention considers calculating earthquake damage coefficients before data transmission and using this calculation to delineate earthquake-affected areas. High-risk areas are specifically identified and analyzed, providing a crucial theoretical basis for subsequent calculations of earthquake data utility values. By achieving precise matching of data delivery, the targeting and accuracy of emergency data transmission are improved, increasing the data acquisition speed and data transmission accuracy for relevant departments.

[0044] Please see Figure 3 , Figure 3 This is a logic block diagram illustrating the division of earthquake-affected areas according to an embodiment of the invention. Specifically, the division of earthquake-affected areas includes, If the earthquake disaster coefficient is greater than the earthquake disaster coefficient threshold, then the earthquake-affected area is classified as a high-risk earthquake-affected area. If the earthquake disaster coefficient is less than or equal to the earthquake disaster coefficient threshold, then the earthquake-affected area is classified as a low-risk earthquake-affected area.

[0045] Specifically, the earthquake damage coefficient threshold represents a boundary that causes damage to a region by an earthquake. It is calculated in advance by obtaining the historical earthquake damage coefficients of several earthquakes that caused damage to the region in advance, and determining the product of the average value of each historical earthquake damage coefficient and the damage coefficient as the earthquake damage coefficient threshold. The damage coefficient is selected in the interval [0.8, 1.0]. In practice, in order to improve the calculation accuracy, the damage coefficient is determined to be 0.9.

[0046] Understandably, "location" refers to the geographical location where the earthquake occurred, and "department" refers to the emergency response agency that needs the data.

[0047] Specifically, the process of determining data dependency representation values ​​includes, Determine the demand keywords for the departmental data and the seismic descriptive terms for the earthquake data. Calculate the semantic relevance between the demand keywords and the earthquake descriptive terms; The semantic relevance is determined as a data dependency representation value.

[0048] Specifically, there are no restrictions on the determination of the required keywords. For example, a multi-dimensional keyword classification system can be constructed, and core words such as 'shaking', 'loud noise', and 'crack' can be predefined by combining expert experience. At the same time, text mining technology can be used to automatically discover and supplement emerging and high-frequency descriptive words such as 'dizziness' and 'running downstairs' from social media data to determine the required keywords. Of course, those skilled in the art can also use other methods to determine keywords, as long as they are reasonable, which will not be elaborated here.

[0049] Specifically, there are no restrictions on the methods for determining semantic similarity. For example, cosine similarity can be used to calculate semantic relevance. Of course, those skilled in the art can also use other methods to determine semantic similarity, as long as they are reasonable. This will not be elaborated further.

[0050] Specifically, the process of analyzing the earthquake impact range at the location to determine the regional disaster situation characterization value includes, Obtain the historical impact range of several earthquakes of the same magnitude to determine earthquake impact factors; The average of the building vulnerability index and the earthquake impact factor at the location is determined as the regional disaster situation characterization value.

[0051] Specifically, there are no restrictions on the method of obtaining the historical impact range. For example, the historical impact range can be determined by obtaining open-source historical earthquake data. Of course, any other reasonable open-source method can also be used, which will not be elaborated here.

[0052] Specifically, the steps for determining earthquake impact factors are as follows. Determine several enclosed areas comprised of the aforementioned historical influence areas; Determine the closed area formed by each of the historical influence ranges, and calculate the union (maximum influence range) and intersection (minimum influence range) of these areas. The ratio of the area of ​​the minimum impact range to the area of ​​the maximum impact range is determined as the earthquake impact factor.

[0053] Specifically, there are no restrictions on how the building vulnerability index is obtained. For example, the vulnerability level can be determined by looking up the building's structural type, construction year, and number of floors in a table. The vulnerability level can then be used as the vulnerability index. Of course, those skilled in the art can also use other methods to determine the building vulnerability index, as long as they are reasonable. This will not be elaborated further. It is understood that the building's structural type, construction year, and number of floors can be obtained from publicly available building manuals.

[0054] Specifically, the process of calculating the utility value of seismic data includes, The ratio of the data dependency representation value to the benchmark data dependency representation value is determined as the first utility factor; The ratio of the disaster situation characterization value of the aforementioned region to the disaster situation characterization value of the benchmark region is determined as the second utility factor; The weighted sum of the first utility factor and the second utility factor is determined to be the utility value of the seismic data.

[0055] Specifically, the baseline data dependency representation value is calculated in advance. Historical data dependency representation values ​​corresponding to several used data are obtained in advance, and the average value of each historical data dependency representation value is determined as the baseline data dependency representation value.

[0056] Specifically, the baseline disaster situation characterization value is calculated in advance. The historical disaster situation characterization values ​​of several locations corresponding to the data to be used are obtained in advance, and the average value of the disaster situation characterization values ​​of each historical region is determined as the baseline disaster situation characterization value.

[0057] Specifically, the sum of the weight coefficients of the first utility factor and the second utility factor is 1. When adjusting the weights, considering that the earthquake situation will affect the urgency of the department's data needs, the weight coefficient of the first utility factor is determined to be 0.6 and the weight coefficient of the second utility factor is 0.4.

[0058] Specifically, by calculating the utility value of earthquake data and packaging the data, a data and theoretical basis is provided for subsequent calculations of matching degree. In practice, due to the massive scale and uneven value density of post-earthquake multi-source heterogeneous data, if all data is transmitted directly without performing utility value analysis, not only will high-timeliness and high-priority core disaster information be overwhelmed by a large amount of low-value data, affecting decision-making efficiency, but also the limited communication bandwidth and computing resources will be occupied by redundant data, hindering the real-time transmission of key information in severely affected areas. Furthermore, due to the large amount of complex data transmitted to relevant departments, the corresponding departments need to expend additional resources to filter effective information, thereby reducing the agility of emergency response. Based on this, this invention considers calculating the utility value of earthquake data, processing data with low relevance and no impact on monitoring locations before data transmission, and only performing subsequent analysis on a portion of the data, thereby improving the accuracy of data analysis, the data acquisition speed of relevant departments, and the accuracy of data transmission.

[0059] Specifically, the process of calculating the matching degree between the department and the data packet includes, Determine the frequency and processing speed of the department's access to the data packet within a predetermined time period; The ratio of the access frequency to the processing speed is determined as the pull matching degree.

[0060] Specifically, in practice, the predetermined time is 30 seconds after the earthquake occurs, so as to quickly identify the core response nodes in the "starting phase" of disaster emergency response. Of course, those skilled in the art can also adjust the predetermined time according to the actual situation, as long as it is reasonable, which will not be elaborated here.

[0061] Specifically, there is no limitation on the method of obtaining the access frequency. For example, the access frequency can be determined by counting the number of calls, message subscriptions or direct query requests made by the department to the data server within a predetermined time period for the data or related topics. The ratio of the number of calls to the predetermined time period is also used. Of course, those skilled in the art can also use other methods to determine the access frequency, as long as they are reasonable. This will not be elaborated further.

[0062] Specifically, there is no limitation on the method of obtaining the processing speed. For example, the processing speed can be determined by statistically analyzing the total time taken by the department to return the processing result after successfully obtaining the data, and the ratio of the total time taken to the number of acquisitions. Those skilled in the art can also use other methods to determine the processing speed, as long as they are reasonable, which will not be elaborated here.

[0063] Please see Figure 4 , Figure 4This is a logical block diagram illustrating the analysis of whether the data packet satisfies a mapping condition to determine whether the department receives the data packet, as per an embodiment of the invention. Specifically, the analysis determines whether the data packet satisfies a mapping condition to determine whether the department receives the data packet, wherein... If the pull matching degree is greater than the pull matching degree threshold, then the data packet is determined to meet the mapping condition, and the department receives the data packet; If the pull matching degree is less than or equal to the pull matching degree threshold, then the data packet is determined not to meet the mapping conditions, and the department does not receive the data packet.

[0064] Specifically, the pull matching degree threshold represents a boundary for the department's data demand. It is calculated in advance by obtaining the historical pull matching degree of the department's data acquisition needs during several earthquakes. The product of the mean of each historical pull matching degree and the pull precision is determined as the pull matching degree threshold. The pull precision is selected within the interval [1.0, 1.2]. In practice, in order to improve the pull accuracy, the pull precision is determined to be 1.1.

[0065] Specifically, by calculating the pull matching degree, the strength of the correlation between data and departmental needs is quantified, thereby providing a basis for data delivery decisions. However, in actual business scenarios, some data access behaviors of departments may only be to meet one-time or occasional needs. If they are judged as high-match objects and mechanically pushed data simply because they have historical pull records, it will not only lead to the network channel and departmental receiving terminals being occupied by a large amount of low-value data, causing significant resource waste, but also cause information overload to drown out truly critical data, thereby reducing the decision-making efficiency and response agility of the overall emergency command system. Based on this, this invention considers introducing a dynamic pull matching degree calculation mechanism. By comprehensively considering access frequency and processing speed indicators, it determines the true pull status between data packets and departments, distinguishes between occasional access and continuous needs, improves the accuracy of data matching, and completes targeted data push, thereby improving the data acquisition speed and data transmission accuracy of relevant departments.

[0066] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0067] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for transmitting multi-source seismic data, characterized in that, include: Historical response parameters of several departments are obtained, including response time, response keywords, and disaster response type, in order to determine the data demand characterization value of each department and to set push labels for each department. Based on the settings of the push tags, earthquake parameters, including magnitude, focal depth, and location, are collected in real time to calculate the earthquake disaster coefficient of each location and delineate earthquake-affected areas. Based on the division of earthquake-affected areas, for high-risk earthquake-affected areas, combined with real-time disaster semantic recognition earthquake data, the dependency relationship between departmental demand data set as push tags and earthquake data is analyzed to determine the data dependency characterization value, and the earthquake impact range of the location is analyzed to determine the regional disaster situation characterization value. The earthquake data utility value is calculated by combining the data dependency characterization value and the regional disaster situation characterization value. The earthquake data that meets the preset conditions is packaged and the successfully packaged data package is pushed to the edge node of the corresponding department. Based on the data packet push status of the edge node, the pull matching degree between the department and the data packet is calculated, and it is analyzed whether the data packet meets the mapping conditions to determine whether the department receives the data packet. The preset condition is that the seismic data utility value is greater than the seismic data utility value threshold.

2. The method for transmitting multi-source seismic data according to claim 1, characterized in that, The process of determining the data requirement representation values ​​for each of the aforementioned departments includes: The ratio of the difference between the response time and the baseline response time is determined as the time influence factor; The matching degree between the response keywords and core earthquake terms is determined as the keyword influence factor; The correlation between the disaster response type and the earthquake type was determined as the type influence factor; The time influence factor, the keyword influence factor, and the type influence factor are normalized. The weighted sum of the normalized time influence factor, keyword influence factor, and type influence factor is determined as the data demand characterization value.

3. The method for transmitting multi-source seismic data according to claim 1, characterized in that, The process involves setting labels for each of the aforementioned departments, wherein... If the data demand representation value is greater than or equal to the data demand representation value threshold, then the department corresponding to the data demand representation value is determined to set a push label; If the data demand representation value is less than the data demand representation value threshold, then the department corresponding to the data demand representation value is determined to set a normal retrieval label.

4. The method for transmitting multi-source seismic data according to claim 1, characterized in that, The process of calculating the earthquake damage coefficient for each location includes... The ratio of the magnitude to the magnitude of the earthquake's impact is determined as the magnitude impact factor; The ratio of the focal depth to the safe focal depth is determined as the depth influence factor; The ratio of the intensity of the earthquake at the location to the seismic fortification intensity is determined as the seismic resistance factor. The weighted sum of the magnitude influence factor, the depth influence factor, and the seismic resistance factor is determined to be the earthquake disaster coefficient.

5. The method for transmitting multi-source seismic data according to claim 1, characterized in that, The earthquake-affected areas are divided as follows: If the earthquake disaster coefficient is greater than the earthquake disaster coefficient threshold, then the earthquake-affected area is classified as a high-risk earthquake-affected area. If the earthquake disaster coefficient is less than or equal to the earthquake disaster coefficient threshold, then the earthquake-affected area is classified as a low-risk earthquake-affected area.

6. The method for transmitting multi-source seismic data according to claim 1, characterized in that, The process of determining the data dependency representation value includes, Determine the demand keywords for the departmental data and the seismic descriptive terms for the earthquake data. Calculate the semantic relevance between the demand keywords and the earthquake descriptive terms; The semantic relevance is determined as a data dependency representation value.

7. The method for transmitting multi-source seismic data according to claim 1, characterized in that, The process of analyzing the earthquake impact range at the location to determine the regional disaster situation characterization value includes, Obtain the historical impact range of several earthquakes of the same magnitude to determine earthquake impact factors; The average of the building vulnerability index and the earthquake impact factor at the location is determined as the regional disaster situation characterization value.

8. The method for transmitting multi-source seismic data according to claim 1, characterized in that, The process of calculating the utility value of seismic data includes: The ratio of the data dependency representation value to the benchmark data dependency representation value is determined as the first utility factor; The ratio of the disaster situation characterization value of the aforementioned region to the disaster situation characterization value of the benchmark region is determined as the second utility factor; The weighted sum of the first utility factor and the second utility factor is determined to be the utility value of the seismic data.

9. The method for transmitting multi-source seismic data according to claim 1, characterized in that, The process of calculating the matching degree between the department and the data packet includes, Determine the frequency and processing speed of the department's access to the data packet within a predetermined time period; The ratio of the access frequency to the processing speed is determined as the pull matching degree.

10. The method for transmitting multi-source seismic data according to claim 1, characterized in that, The analysis determines whether the data packet satisfies the mapping conditions to ascertain whether the department has received the data packet. If the pull matching degree is greater than the pull matching degree threshold, then the data packet is determined to meet the mapping condition, and the department receives the data packet; If the pull matching degree is less than or equal to the pull matching degree threshold, then the data packet is determined not to meet the mapping conditions, and the department does not receive the data packet.

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